DIFFERENCES IN FORENSIC SCIENCE TRAINING AMONG THE INVESTIGATING POLICE OFFICERS IN NIGERIA

Training remains an important activity which organisations should embrace to ensure achievement of organizational goals. With modernization and development in science and technology, police organisations should train their personnel in the area of forensic investigation, because it is a fair and reliable means of understanding how crimes and related issues occurred. Sociodemographic factors play an important role in determining who goes for training among workers in an organisations; this could apply to the Nigeria Police (NP) agency. The aim of this study was to understand whether there were differences in the attendance of forensic science training among the Investigating Police Officers (IPOs) in the NP, based on their sociodemographic characteristics, namely, gender, age, marital status, highest educational qualification, rank and years spent in service. Using sample survey method, information related to the frequency of forensic science training attendance and sociodemographic profiles of 401 IPOs was collected. SPSS was used to analyze the data. Although forensic training was generally infrequent among the IPOs, the results showed that the frequency of training attendance was not similar among the categories of age, marital status, highest educational qualification and years spent in service, but was the same in the categories of gender and rank of the IPOs. It is recommended that sociodemographic profiles of IPOs need to be properly considered in selecting who should attend forensic training among the IPOs.  Article visualizations

Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring.

The application of Deep Neural Networks (DNNs) for monitoring cyberattacks in Internet of Things (IoT) systems has gained significant attention in recent years. However, achieving optimal detection performance through DNN training has posed challenges due to computational intensity and vulnerability to adversarial samples. To address these issues, this paper introduces an optimization method that combines regularization and simulated micro-batching. This approach enables the training of DNNs in a robust, efficient, and resource-friendly manner for IoT security monitoring. Experimental results demonstrate that the proposed DNN model, including its performance in Federated Learning (FL) settings, exhibits improved attack detection and resistance to adversarial perturbations compared to benchmark baseline models and conventional Machine Learning (ML) methods typically employed in IoT security monitoring. Notably, the proposed method achieves significant reductions of 79.54% and 21.91% in memory and time usage, respectively, when compared to the benchmark baseline in simulated virtual worker environments. Moreover, in realistic testbed scenarios, the proposed method reduces memory footprint by 6.05% and execution time by 15.84%, while maintaining accuracy levels that are superior or comparable to state-of-the-art methods. These findings validate the feasibility and effectiveness of the proposed optimization method for enhancing the efficiency and robustness of DNN-based IoT security monitoring

Resource efficient boosting method for IoT security monitoring.

Machine learning (ML) methods are widely proposed for security monitoring of Internet of Things (IoT). However, these methods can be computationally expensive for resource constraint IoT devices. This paper proposes an optimized resource efficient ML method that can detect various attacks on IoT devices. It utilizes Light Gradient Boosting Machine (LGBM). The performance of this approach was evaluated against four realistic IoT benchmark datasets. Experimental results show that the proposed method can effectively detect attacks on IoT devices with limited resources, and outperforms the state of the art techniques

Memory efficient federated deep learning for intrusion detection in IoT networks.

Deep Neural Networks (DNNs) methods are widely proposed for cyber security monitoring. However, training DNNs requires a lot of computational resources. This restricts direct deployment of DNNs to resource-constrained environments like the Internet of Things (IoT), especially in federated learning settings that train an algorithm across multiple decentralized edge devices. Therefore, this paper proposes a memory efficient method of training a Fully Connected Neural Network (FCNN) for IoT security monitoring in federated learning settings. The model‘s performance was evaluated against eleven realistic IoT benchmark datasets. Experimental results show that the proposed method can reduce memory requirement by up to 99.46 percentage points when compared to its benchmark counterpart, while maintaining the state-of-the-art accuracy and F1 score

Effect of Paraquat Herbicide on Oxidative Stress Biomaker Enzyme Activities in C. Gariepinus

Toxicity assessment was conducted for 96hr exposure duration using synthetic herbicide (paraquat dichloride 276g/L) on Claris gariepinus with mean weight range of 27.2 - 29.7g and mean length 10.95 -15.5cm. They were exposed to varying herbicide concentrations of 0.0, 3.45, 6.90, 10.35 and 13.5mg/L with 5-levels exposure concentrations in a Completely Randomized Design (CRD). Liver, gills and kidney tissues were analyzed for oxidative stress enzymes activities using Solarbio science assay kit (BC1170, 0170 and 0020). Four days lethal concentration (LC50) value for 96hr was found to be 7.298mg/L. The treated fish displayed erratic swimming with irregular opercular movement, loss of reflex, mucus secretion and increased air gulping with the increasing concentration of the herbicide compared with the control fish. Antioxidant biomarkers activities revealed that Glutathione S-transferase (GST), catalase (CAT) and superoxide dismutase (SOD) activities increased significantly (P<0.05) in the gills, liver and kidney tissues at higher concentrations compared with control. It can be deduced that alterations in the oxidative stress enzyme activities in the exposed fish to paraquat exert toxic effect on the liver, gills and kidney tissues. It is therefore recommended that appropriate authorities should develop strategies on minimizing the indiscriminate use of synthetic herbicides due to their impact on aquatic biota such as fish in order to reduce its potential risk to other non-target organisms. Keywords: Clarias gariepinus, Lethal concentration, Oxidative Stress enzymes, Paraquat, Toxicity assessmen

H55N polymorphism is associated with low citrate synthase activity which regulates lipid metabolism in mouse muscle cells

Funding: This work was supported, in whole or in part, by European Social Fund under the Global Grant measure Grant VP1-3.1-ŠMM-07-K-02-057 (to A.L.), European Foundation for the Study of Diabetes grant (to T.V.), NHS Grampian Endowment grant (to A.R. and S.R.G.), Kuwait Ministry of Health grant (to M.A.), Saudi Ministry of Higher Education grant (to Y.A.,) as well as Saltire scholarship, Wenner-Gren Foundation Postdoctoral Fellowship, Albert Renold Travel Fellowship and a Novo Nordisk Foundation Challenge Grant (to B.G.).Peer reviewedPublisher PD

Heavy metals bioaccumulation in tissues of Tilapia zilli as indicators of water pollution in kafinchiri reservoir, Kano - Nigeria

This study assessed the levels of heavy metals accumulation in water, gills and liver of Tilapia zilli fish collected from Kafinchiri water Reservoir for a period of four months with the aim of predicting health risk effect on consumers. Water and Tilapia zilli samples were collected from three different sites along the course of the dam; upstream, midstream and downstream. The concentration of copper, lead, chromium and cadmium in water and their accumulation in the liver and gills of the sampled fishes were determined using atomic absorption spectrophotometer. The results revealed that concentration of dissolved heavy metals in the water ranges from Cu (0.4mg/L- 0.6mg/L), Pb (0.9 mg/L – 1.4mg/L), Cr (undetected - 0.1mg/L) and Cd (0.01mg/L – 0.02mg/L). Accumulation in the gills of tilapia fish ranges from Cu (0.8μg/g – 0.85μg/g), Pb (0.3μg/g -0.9μg/g), Cr (≤0.1μg/g) and Cd was not detected. The accumulation of heavy metals in the liver were Cu (3.0μg/g – 5.4μg/g), Pb (2.7μg/g – 9.6μg/g) and Cr (0.1μg/g – 0.15μg/g) and Cd not detected. Water content chemical analysis indicated that; sampling point B (midstream) had the highest concentration of the heavy metals in which Pb recorded had the highest Bioaccumulation factor (BAF) of 5.76. The mean range of physicochemical parameters studied were temperature (25.90 – 27.37 °C), pH (7.60 – 8.52), DO (6.27 – 7.47mg/L), BOD (2.02 – 3.02mg/L), turbidity (28.05 - 34.00 NTU), electrical conductivity(187.60 – 361.17μS/cm), TDS (211 - 363mg/L), Total dissolved solids, electrical conductivity, turbidity and nitrate recorded significant difference between sites (P<0.05). It was believed that domestic activities around the reservoir is the major contributing factor to the accumulation of toxic heavy metals in fish examined. It is recommended that intervention by relevant authorities is needed curtail potential long term effect of this pollutants in the reservoir.Key words: Heavy metals Pollution, Tilapia zilli, Bioaccumulation, Kafinchiri Reservoi

Design and Construction of an Automatic Home and Office Power Control System

In homes and offices, it is very common for occupants to forget to switch OFF the lighting and fans when leaving the premises. This can be attributed to human forgetfulness and the epileptic power supply which causes interruption that results in users forgetting the state of their appliances (whether they are ON or OFF). Consequently, these appliances would continue to work whenever power is restored when the occupants might have vacated the premise. This action is not a small contributor to energy wastage in a country like Nigeria where there is an inadequate energy supply to go round the populace. In this work, a simple but robust automatic home and office power control system is developed to auto-detect the presence of an occupant in the room through the passive infrared (PIR) sensor and control the electrical appliances (lighting and fan source) in the room. Certain conditions must be met for the operation of lighting and the fan source. The lighting comes up when the PIR sensor senses the presence of an occupant and the room is in darkness, while the fan would work when there is an occupant and the temperature in the room is above 35 °C. These conditions are programmed to suit the need of the occupant but cannot be changed by the user. The device automatically switches OFF within five minutes after the last occupant leaves the room

Reducing computational cost in IoT cyber security: case study of artificial immune system algorithm.

Using Machine Learning (ML) for Internet of Things (IoT) security monitoring is a challenge. This is due to their resource constraint nature that limits the deployment of resource-hungry monitoring algorithms. Therefore, the aim of this paper is to investigate resource consumption reduction of ML algorithms in IoT security monitoring. This paper starts with an empirical analysis of resource consumption of Artificial Immune System (AIS) algorithm, and then employs carefully selected feature reduction techniques to reduce the computational cost of running the algorithm. The proposed approach significantly reduces computational cost as illustrated in the paper. We validate our results using two benchmarks and one purposefully simulated data set

Resource efficient federated deep learning for IoT security monitoring.

Federated Learning (FL) uses a distributed Machine Learning (ML) concept to build a global model using multiple local models trained on distributed edge devices. A disadvantage of the FL paradigm is the requirement of many communication rounds before model convergence. As a result, there is a challenge for running on-device FL with resource-hungry algorithms such as Deep Neural Network (DNN), especially in the resource-constrained Internet of Things (IoT) environments for security monitoring. To address this issue, this paper proposes Resource Efficient Federated Deep Learning (REFDL) method. Our method exploits and optimizes Federated Averaging (Fed-Avg) DNN based technique to reduce computational resources consumption for IoT security monitoring. It utilizes pruning and simulated micro-batching in optimizing the Fed-Avg DNN for effective and efficient IoT attacks detection at distributed edge nodes. The performance was evaluated using various realistic IoT and non-IoT benchmark datasets on virtual and testbed environments build with GB-BXBT-2807 edge-computing-like devices. The experimental results show that the proposed method can reduce memory usage by 81% in the simulated environment of virtual workers compared to its benchmark counterpart. In the realistic testbed scenario, it saves 6% memory while reducing execution time by 15% without degrading accuracy