In cloud computing environments, the representation and management of data through workflows are crucial to ensuring efficient processing. This paper focuses on securing scientific workflow scheduling, which involves executing complex data-processing tasks with specific dependencies. The security of intermediate data, often transmitted between virtual machines during workflow execution, is critical for maintaining the integrity and confidentiality of scientific workflows. This review analyzes methods for securing scientific workflow scheduling in cloud environments, emphasizing the application of security principles such as confidentiality, authentication, and integrity. Various scheduling algorithms, including heuristics and metaheuristics, are examined for their effectiveness in balancing security with constraints like execution time and cost.

This paper proposes an algorithm that enhances horizontal handover (HO) in dense wireless local area networks (WLANs), which is implemented in a software-defined wireless networking (SDWN)-based architecture. The algorithm considers the concept of user prioritisation, classifying the WLAN stations (STAs) into two categories representing high and low priorities respectively, and always attempts to guarantee the best quality of experience (QoE) to the high priority users. The architecture that implements the algorithm leverages the flexibility, programmability, and centralised nature of SDWN to efficiently manage the HO process. Moreover, the paper presents a performance evaluation campaign that demonstrates significant achievements against a state-of-the-art solution in terms of the provided QoE, throughput and delay. Finally, we discuss the importance of considering user prioritisation in a HO algorithm for dense WLANs.

In grid-deficient environments, residential energy systems face severe carbon emission penalties due to mandatory reliance on diesel standby generators during supply interruptions. In Iraq, summer peak loads routinely exceed grid capacity, triggering prolonged generator operation and dramatically increasing household carbon footprints. This study presents a deep Q-network (DQN) reinforcement learning framework for intelligent battery energy storage system (BESS) scheduling, targeting carbon emissions reduction through strategic peak shaving. The DQN agent learns optimal battery dispatch strategies by internalizing diurnal patterns in load and solar generation through temporal state features, enabling anticipatory control without requiring explicit external forecasting models. The system is trained on one-year operational data from a representative Iraqi residential installation and evaluated over the critical summer period (122 days, 35.5% grid unavailability). The results demonstrate a 54.8% ${\mathrm{CO}}_2$ reduction (306.5 kg versus 677.4 kg baseline), a 25.5% reduction in generator runtime, and a 23.7% reduction in operating costs for the studied configuration. The learned policy approaches 89.6% of perfect-foresight MILP performance while executing 35,000 times faster. A reward function sensitivity analysis across five weighting schemes confirms that the 20:1 carbon-to-cost priority ratio optimally balances environmental and economic objectives. Ablation studies quantify the mechanism contributions: anticipatory pre-charging accounts for 58% of the total improvement, discharge optimization for 44%, and real-time PV coordination for 22%. These findings establish DQN-based BESS optimization as a practically deployable decarbonization approach for residential systems in grid-constrained developing regions.

Person identification is a pivotal challenge in the security domain, with important and impactful applications such as identifying crime suspects and locating missing persons. One convenient person identification method is gait identification, where individuals are identified by their unique walking style. However, traditional methods of gait identification are often affected by variations in appearance and occlusion. This work introduces a novel and robust spatiotemporal kinematics‐informed non‐invasive gait identification (STONI‐GID) method that uses human pose estimation, occlusion state estimation and deep machine learning. Furthermore, unlike some existing methods, we demonstrate that our method remains unaffected by everyday appearance changes, environment, or viewing angle. Our approach achieved identification accuracy of up to 98.66% when evaluated using our primary dataset of 65 diverse participants in real‐world environments. Moreover, the model outperformed existing methods during cross‐dataset validation on the large Southampton dataset and the Gait Recognition Image and Depth Dataset (GRIDDS), achieving identification accuracies of 97.68% and 99.12%, respectively. Our findings will particularly advance the research frontiers of real‐world gait identification and impact interdisciplinary areas of security and healthcare applications.

Adolescent mental health difficulties represent a significant public health concern. This study aimed to identify prevalence rates and predictors of suicide ideation, depressive symptoms, and symptoms of post-traumatic stress disorder (PTSD) among Iraqi adolescents. A cross-sectional quantitative survey design was used. A total of 469 adolescents aged 12–20 years (M = 16.25) participated across eight high schools in Baghdad. Multiple regression models were used to examine predictors. Mean depressive (M = 12.99) and PTSD (M = 30.98) symptom scores were in the ‘moderate’ range, with the majority of participants scoring in the ‘moderate’ or ‘severe’ range (73.1% and 75.5%, depressive and PTSD symptoms, respectively). A total of 12.6% of young people scored in the ‘high risk’ range for suicidal behavior. Females reported higher depressive symptoms and higher PTSD symptoms. Older age was negatively associated with suicide ideation but positively associated with PTSD symptoms. Quality of life emerged as the strongest and most consistent predictor across all models. Hopelessness predicted suicide ideation, while PTSD symptoms predicted depression. In summary, adolescents were experiencing clinically elevated rates of depressive and PTSD symptoms on average. A significant minority were also experiencing suicidal thoughts. Interventions that enhance functioning, reduce hopelessness, and incorporate trauma-informed approaches may be effective targets for prevention and early intervention.