Research Articles

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Development of information system for enhancing communication between members of parliament and citizens: A study of dodoma, Tanzania
(Elsevier Inc., 2026-04-12) Mtoi, Mary; Ally, Mussa; Sam, Anael; Mbelwa, Hope
In Tanzania, members of parliament rely on both traditional channels and social media to gather citizens’ concerns. However, limited access to internet-enabled devices, the unavailability of members of parliament, poor attendance at public meetings, and high internet costs create a communication gap in civic participation. Existing civic technology solutions often depend on internet-based platforms, excluding users who rely on feature phones and highlighting a technical gap in prior research and system design. This study develops a context-aware, inclusive digital communication system that bridges infrastructural and technological barriers to foster civic engagement in low-resource environments. Through a literature review, gaps in existing civic technology implementations were identified, and the system was developed using the Extreme Programming Agile Methodology to produce a hybrid platform combining a web application and a two-way messaging system. The resulting Public Participation Information System allows citizens to raise concerns offline via feature phones or online through a bilingual web application in Swahili and English. The system prioritizes accessibility for low-resource users and incorporates features such as availability scheduling, task delegation, and concern tracking. Feedback indicates that it effectively reaches under-connected populations and promotes more inclusive civic engagement. Overall, the system addresses the digital divide in civic communication and offers a scalable framework that integrates low-technology access with modern information systems to support democratic participation in resource-constrained settings.
Chance-Constrained Distributed Optimization with Shared States
(MDPI, 2026-04-23) Mugenga, Ineza Remy; Geletu, Abebe; Mirau, Silas; Li, Pu
Interconnected systems have attracted significant attention in numerous engineering applications such as energy, water, and oil, as well as gas distribution networks. However, due to their high complexity, it is enormously difficult to ensure a reliable and effective cooperation of such interconnected systems under limited communication and interaction capacities. In addition, uncertainty consideration poses further challenges in developing an efficient distributed optimization approach to such interconnected systems. Furthermore, satisfying the constraints of shared states between subsystems under uncertainty leads to conflict issues and has not been properly studied yet. This study proposes a chance-constrained distributed optimization approach to the optimal operation of interconnected systems by considering conflicting reliability levels of satisfying shared state constraints. A compromised reliability level for such constraints is determined by an averaged weighting. We establish that the optimal cost is Lipschitz-continuous with respect to the compromised reliability level, providing a theoretical basis for quantifying the marginal cost of reliability, and we show that the compromise is robust to perturbations in the subsystem weights. We develop a numerical solution framework based on the inner–outer approximation method. For the efficient computation of the high-dimensional integrals, we use Hoeffding’s inequality to determine a suitable sample size. The optimal operation of three interconnected energy distribution networks is used as a case study to demonstrate the proposed approach.
Agent-based simulation of seasonal malaria chemoprevention strategy in Southern Tanzania: comparing dihydroartemisinin-piperaquine with or without primaquine
(Springer Nature, 2026-02-13) Mfala, Celina; Nyambo, Devotha; Mwaiswelo, Richard; Tchuenche, Jean; Clemen, Thomas
Background The effect of seasonal malaria chemoprevention (SMC) strategy on malaria transmission using a single low dose of primaquine (SLDPQ) added to artemisinin-based combination therapy has not been established in Africa. An agent-based model and simulation (ABMS) was used to assess SMC effectiveness using dihydroartemisinin-piperaquine (DP) with and without SLDPQ in Masasi and Nanyumbu Districts, Tanzania. Methods ABMS was developed in AnyLogic platform using secondary data from a cluster-randomized DP-based SMC study conducted in the districts, to assess the effectiveness of DP with and without SLDPQ for control of malaria in under-five children. The model incorporated human, mosquito, transmission, intervention, and environment sub-models, and simulated three monthly rounds of SMC over a 180-day period. Environment temperature, an important factor in mosquito breeding was simulated in three scenarios, first using average field temperature, and then when it was increased or decreased by 10C from the average. Model outputs were compared with field results to evaluate external validity. Results Overall, 2275 participants, 1135 in the intervention and 1140 in the control arm were involved in the model. At baseline, malaria prevalence was 11.5% (130/1135) and 16.3% (186/1140) in the intervention and control arm, respectively. At the end of 125-day simulation period malaria prevalence declined to 4.1% (47/1135), and it rebounded to 7.1% (80/1135) at the end of 180-day simulation period after three rounds of DP alone administration. Addition of SLDPQ to DP led to a further declined of the prevalence to 1.4% (16/1135) and 3.9% (44/1135) at the end of 125-day and 180-day, respectively. In the DP alone, the increase in average temperature by 1˚C further decreased malaria prevalence to 2.6% (30/1135) and 5.0% (57/1135) at the end of 125-day and 180-day, respectively, whereas the decrease of temperature by 1 ˚C decreased the malaria prevalence to 3.2% (36/1135) and 4.2% (48/1135) at the end of 125-day and 180-day, respectively. Conclusions The ABMS has demonstrated that addition of SLDPQ to DP reduced malaria transmission significantly regardless of the increase or decrease of the average temperature by 1 ˚C. SLDPQ can be added to DP-SMC and scaled-out for the control of malaria in Tanzania.
Adaptive Decision-Level Intrusion Detection for Known and Zero-Day Attacks
(MDPI, 2026-04-09) Mchina, Joseph; Mduma, Neema; Sinde, Ramadhani
Network Intrusion Detection Systems (NIDS) face increasing challenges from sophisticated cyber threats, particularly zero-day attacks that evade signature-based methods. While supervised learning is effective for known attack classification, it struggles with novel threats, whereas anomaly-based approaches suffer from high false positive rates and unstable thresholds. To address these limitations, this paper proposes a decision-level adaptive intrusion-detection framework combining hierarchical CNN-based closed-set classification with autoencoder-based zero-day detection in a cascade architecture. The framework enables deployment-time adaptation by dynamically adjusting class-specific confidence thresholds and fusion parameters without model retraining. Experiments on the CSE-CIC-IDS2018 dataset demonstrate strong closed-set performance, achieving 98.98% accuracy and a macro-F1-score of 0.9342, with improved recall for minority attack classes under adaptive thresholding. Under a zero-day evaluation protocol in which Web_Attacks and Infiltration are excluded from training and validation, the proposed approach achieves an F1-score of 0.9319 while maintaining a low false positive rate of 0.0019. The framework is further evaluated on the Simulated University Network Environment (SUNE) dataset representing campus network traffic, achieving 96.18% closed-set accuracy and 97.54% accuracy in the integrated cascade setting. These results demonstrate that the proposed framework effectively balances minority attack detection, zero-day identification, and false-alarm control in dynamic and resource-constrained network environments.
A Mathematical Model for Smooth Muscle Cell Phenotype Switching In Atherosclerotic Plaque
(Springer Nature, 2026) Ndenda, Joseph; Watson, Michael; Misra, Ashish; Myerscough, Mary
Smooth muscle cells (SMCs) play a fundamental role in the development of atherosclerotic plaques. They ingest lipids in a similar way to monocyte-derived macrophages (MDMs) in the plaque. This can stimulate SMCs to undergo a phenotypic switch to a macrophage-like phenotype. We formulate an ordinary differential equation (ODE) model for the populations of SMCs, MDMs and smooth muscle cell-derived macrophages (SDMs) and the internalised lipid load in each population. We use this model to explore the effect on plaque fate of SMC phenotype switching. We find that when SMCs switch to a macrophage-like phenotype, there is an increase in the lipid quantity in the model plaque that is internalised inside cells. Additionally, removal of SMCs from the model plaque via phenotype switching reduces the number of SMCs in the fibrous cap, increases the lipid in the necrotic core, and increases plaque inflammation. These features are hallmarks of vulnerable plaques, whose rupture can cause heart attacks or strokes. When SDMs are highly proliferative or resistant to cell death, the model plaque becomes increasingly pathological. The model suggests that the switch of SMCs to a macrophage-like phenotype may drive the development of unstable and pathological plaques.
A Hybrid Machine Learning and Signature-Based Approach for Detecting Network Pivoting in BYOD Environments
(Asosiasi Doktor Sistem Informasi Indonesia, 2026-02-25) Amour, Nassor; Leo, Judith; Dida, Mussa
This study addresses the challenge of detecting network pivoting, a lateral movement technique that is difficult to identify in insider and BYOD environments because malicious transitions can resemble normal internal activity. The objective was to improve detection of both known and unknown pivoting behaviours while supporting practical triage in resource-constrained institutions. A hybrid detection framework was developed that fuses Snort signature alerts with machine learning classification and unsupervised anomaly detection using behavioural features derived from BYOD-like network traffic. The approach was evaluated in a controlled testbed and supported by organisational survey findings on awareness and monitoring practice. Results show the hybrid system achieved 96.2% classification accuracy with a 4.5% false positive rate when distinguishing normal traffic, suspicious activity, and pivoting attacks. Compared with signature-only and machine-learning-only baselines, the hybrid design detected simulated pivoting attempts earlier and more consistently. User acceptance testing also reported strong satisfaction with the integrated dashboard for monitoring, filtering, and reporting. The key contribution is a unified, dashboard-oriented fusion of signature and behavioural evidence that strengthens early lateral movement detection and reduces manual correlation effort.
Modeling and evaluating targeted interventions to curb avian influenza spread among humans and domestic birds
(Communications in Mathematical Biology and Neuroscience, 2026-02-13) Soka, Serapia; Kgosimore, Moatlhodi; Mayengo, Maranya
Avian influenza, caused by influenza A viruses, has drawn substantial attention globally due to increased deaths from time to time. This study examines control interventions for avian influenza transmission in humans and domestic birds using a deterministic mathematical model. The model incorporates three time-dependent interventions: vaccinating susceptible birds, maintaining proper hygiene practice, and culling infected birds. The next-generation matrix method is used to determine the effective reproduction number. Optimal control theory is applied by incorporating: vaccination, proper human hygiene practices, and culling of infected birds. To determine the necessary conditions for the existence of optimal controls, the Pontryagin’s Maximum Principle is employed. The optimal control problem is then solved using the forward-backward sweep method based on the fourth-order Runge-Kutta algorithm, implemented in MATLAB. Results from the optimal control analysis show that the strategy combining all interventions is the most effective approach for controlling the disease. Furthermore, the Incremental Cost-Effectiveness Ratio (ICER) is used to identify the most cost-effective strategy for disease control. The findings show that Strategy VII has a negative ICER, indicating that it is less costly and averts more infections. Therefore, to eliminate the disease, we recommend the implementation of all the aforementioned control measures.
Towards Self-Defending SDN Infrastructures: Real-Time Honeypot-Enabled Botnet Detection Using ONOS
(Asosiasi Doktor, 2026-02) Kaare, Nyamwaga; Sam, Anael
Modern Software-Defined Networks (SDNs), while benefiting from centralized programmability, remain vulnerable to fast-evolving botnet attacks. This paper presents and evaluates a lightweight ONOS-based honeypot and decoy framework designed to detect and automatically block multi-vector botnet behaviors in real time. The system integrates honeypot-exposed Telnet, SMB, and DNS services with threshold-, entropy-, signature-, and correlation-based inspection within a tree topology (depth = 2, fanout = 4) consisting of five OpenFlow switches and 50 hosts. Quantitatively, the system achieved 100% detection of all signature-based attacks (55/55), 100% blocking of distributed UDP scans (50/50), and 0% false positives on benign decoy access. Median detection latency ranged between 1–3 seconds. True positives (TP), false negatives (FN), false positives (FP), and true negatives (TN) were measured using ground-truth attacker lists built into automated test scripts, yielding precision and recall of 1.00 across all malicious scenarios. This work demonstrates that combining deception with SDN-level flow automation enables effective and computationally efficient botnet defense without machine learning. A key limitation is that all evaluations were conducted exclusively in a controlled Mininet simulation, which may not fully represent real-world traffic dynamics. Future work will validate the system on physical SDN deployments and evaluate its robustness under production workloads.
Policy-practice gaps and barriers to the effective management of insecticide resistance in Tanzania
(Research Square, 2026-02-01) Gervas, Hamenyimana; Moshi, Irene; Shirima, GloriaSalome; Nambunga, Ismail; Njalambaha, Rukiyah; Mohamed, Sheikha; Nshama, Rosemary; Mwalimu, Charles; Mayengo, Maranya; Mlacha, Yeromin; Selvaraj, Prashanth; Okumu, Fredros; Ngowo, Halfan
Background Malaria control gains are stagnating or reversing, in part due to widespread insecticide and drug resistance, as well as increasing nancial and implementation challenges. This study assessed the national strategies for insecticide resistance monitoring and management in Tanzania to identify key barriers and misalignments between policy and actual practice. Methods We initially reviewed technical documents from Tanzania’s National Malaria Control Programme (NMCP) produced between 2014 and 2024, along with World Health Organization (WHO) guidelines and reports on insecticide resistance monitoring and management. This was followed by in-depth interviews with key informants in Tanzania, including policymakers, representatives from funding agencies, technical experts, scientists, and vector control implementers at district and regional levels. The qualitative data was analyzed thematically using NVivo software. Findings The document review revealed strong policy alignments of the NMCP strategies for monitoring and management of insecticide resistance with international practice as recommended by WHO. However, implementation of the policy intentions remained limited, with resistance monitoring being conducted in only 22 of Tanzania’s 184 district councils. Interviews with 24 stakeholders highlighted signi cant gaps between the stated policies and guidelines and the actual practice. These gaps were driven by inadequate nancing, donor dependence, insu cient coordination and dissemination of guidelines, limited technical capacity, and weak engagement of communities and district-level operators. Participants emphasized that without strengthened surveillance systems, sustainable local nancing, and greater community and intersectoral collaboration, achieving the current strategic goal of malaria elimination by 2030 remains unlikely. Conclusion Despite strong policy alignment and strategic planning, Tanzania's implementation of insecticide resistance management remains limited. Bridging these persistent policy-practice gaps is essential for more effective malaria vector control, and will require sustained nancing, strengthened coordination, improved vector monitoring, and enhanced community and intersectoral engagement.
LSTM-based deep reinforcement learning for ISI mitigation in maritime LoRaWAN
(Elsevier B.V., 2026-02-28) Lyimo, Martine; Mgawe, Bonny; Leo, Judith; Dida, Mussa; Michael, Kisangiri
For reliable, long-range, low-power Maritime Internet of Things (MIoT) communication (e.g., vessel tracking, ocean monitoring, and offshore automation), LoRaWAN offers attractive coverage and energy efficiency. How ever, sea-surface reflections, wave motion, and platform mobility create time-varying multipath with large delay spread, which induces inter-symbol interference (ISI) and degrades packet delivery ratio (PDR) and energy performance. This paper proposes a Long Short-Term Memory (LSTM)-assisted Deep Reinforcement Learning (DRL) framework LSTM–DDPG Adaptive Modulation and Coding (LD-AMC) that proactively mitigates ISI by predicting short-term channel evolution and adapting the LoRaWAN physical-layer parameters. An LSTM pre dictor learns temporal correlations in observed link metrics (RSSI, SNR, PER, and RMS delay spread) and pro vides one-step-ahead forecasts, which are appended to the agent state. A Deep Deterministic Policy Gradient (DDPG) controller then selects the spreading factor (SF), coding rate (CR), bandwidth (BW), and transmit power (P ) within LoRaWAN constraints to maximize a reward that favors reliable delivery and throughput while penalizing energy cost and ISI severity. MATLAB/Simulink simulations under coastal and offshore two-ray maritime channels show that LD-AMC reduces ISI-induced symbol errors by up to 58%, improving PDR by up to 47% and reducing energy per delivered packet by up to 32% compared with standard and enhanced ADR baselines.
High-resolution UAV dataset for identification of breeding sites for malaria and dengue vectors in rural and peri-urban Tanzania: An experimental approach with Gemma 3N E4B architecture
(Elsevier Inc., 2026-02-26) Nyambo, Devotha; Lyimo, Tumaini; Sauli, Elingarami
Malaria and Dengue are the major vector borne diseases (VBDs) with high prevalence in the tropics, where their existence is significantly linked to the changing climatic conditions which favour the continuous breeding of vectors. In Tanzania, the burden for malaria is more pronounced in rural and peri‑urban settings than in urban, with prevalence of up to 24% for under 5 children, varying across geographical zones. This paper presents a high-resolution UAV dataset collected from Mtwara and Tabora regions in southern and western zones, respectively, for development of surveillance models for potential mosquito breeding sites. The presented dataset contains 19 classes of mosquito breeding sites that were identified from 471 aerial images as a 80.5% of retained images with useful information from the total of 585 collected in Igunga and Masasi Districts in Tabora and Mtwara regions, respectively. With an experimental approach, the paper presents the use of the imagery dataset on the state of art Gemma 3 N E4B, an instruction-tuned multimodal architecture capable of processing both visual and textual data. By using the 80.5% proportion remained after collaborative image labelling, we trained and validated the instructional model with exceptional convergence characteristics and a stabilization phase yielding a loss of 0.0319. The reduction in loss was 96.7%, from 9.620 to 0.319, within 117 steps.
Global stability analysis of a fuzzy HIV–TB co-infection model with environmental TB transmission
(Elsevier B.V., 2026-03-27) Luambano, Dickson; Stephano, Mussa; Mayengo, Maranya
Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) co-infection remains a significant global health challenge, further complicated by environmental transmission and inherent biological uncertainties. This study introduces a novel fuzzy compartmental model that captures these complexities by representing key transmission and mortality rates as fuzzy parameters depending on viral and bacterial loads. We establish the model’s well-posedness and derive fuzzy basic reproduction numbers. Using Lyapunov-based analysis combined with matrix- theoretic and graph-theoretic methods, we analytically establish a global stability threshold: the disease is eliminated when the basic reproduction numbers are below unity and persists otherwise. To assess empirical relevance, both the fuzzy system and its deterministic counterpart were independently calibrated to WHO Tanzania HIV-TB co-infection data (2000-2023). The deterministic model achieved strong pointwise accuracy (RMSE = 13.21, 𝑅2 = 0.977), whereas the fuzzy framework captured 87.5% of observations within its uncertainty band, reflecting het- erogeneity in transmission and mortality processes. Global sensitivity analysis further identified transmission-related parameters as the dominant drivers of infection dynamics, while treatment rates and environmental clearance emerged as key factors in disease control. Numerical simulations further confirm that increased antiretroviral therapy and environmental bacterial clearance significantly reduce infection burden. These findings emphasize the biological sig- nificance of viral load suppression and environmental control in reducing HIV-TB co-infection burden. By explicitly incorporating biological uncertainty, the fuzzy formulation offers a more realistic and policy-relevant analytical framework compared to purely deterministic models. The proposed approach provides a flexible tool for uncertainty-aware epidemiological assessment and integrated HIV-TB intervention planning.
Global sensitivity analysis of catheter-associated urinary tract infection models using the eFAST method
(2026-02-26) Nyambo, Devotha; Lyimo, Tumaini; Sauli, Elingarami
Malaria and Dengue are the major vector borne diseases (VBDs) with high prevalence in the tropics, where their existence is significantly linked to the changing climatic conditions which favour the continuous breeding of vectors. In Tanzania, the burden for malaria is more pronounced in rural and peri‑urban settings than in urban, with prevalence of up to 24% for under 5 children, varying across geographical zones. This paper presents a high-resolution UAV dataset collected from Mtwara and Tabora regions in southern and western zones, respectively, for development of surveillance models for potential mosquito breeding sites. The presented dataset contains 19 classes of mosquito breeding sites that were identified from 471 aerial images as a 80.5% of retained images with useful information from the total of 585 collected in Igunga and Masasi Districts in Tabora and Mtwara regions, respectively. With an experimental approach, the paper presents the use of the imagery dataset on the state of art Gemma 3 N E4B, an instruction-tuned multimodal architecture capable of processing both visual and textual data. By using the 80.5% proportion remained after collaborative image labelling, we trained and validated the instructional model with exceptional convergence characteristics and a stabilization phase yielding a loss of 0.0319. The reduction in loss was 96.7%, from 9.620 to 0.319, within 117 steps.
Graph Convolution Neural Network and Deep Q-Network Optimization-Based Intrusion Detection with Explainability Analysis
(MDPI, 2026-02-24) Mwiga, Kelvin; Dida, Mussa; Maglaras, Leandros; Mohsin, Ahmad; Janicke, Helge; Sarker, Iqbal
As networks expand in size and complexity, coupled with an exponential increase in intrusions on network and IoT systems, this leads to traditional models failing to cap ture increasingly intricate correlations among network components accurately. Graph Convolution Networks (GCNs) have recently acquired prominence for their capacity to represent nodes, edges, or entire graphs by aggregating information from adjacent nodes. However, the correlations between nodes and their neighbours, as well as related edges, differ. Assigning higher weights to nodes and edges with high similarity improves model accuracy and expressiveness. In this paper, we propose the GCN-DQN model, which integrates GCN with a multi-head attention mechanism and DQN (Deep Q Network) to adaptively adjust attention weights optimizing its performance in intrusion detection tasks. After extensive experiments using the UNSW NB15 and CIC-IDS2017 dataset, the proposed GCN-DQNoutperformed the baseline model in classification accuracy. We also applied LIME and SHAPtechniques to provide explainability to our proposed intrusion detection model.
Global sensitivity analysis of catheter-associated urinary tract infection models using the eFAST method
(2026-03-04) John, Innocent; Stephano, Mussa; Mayengo, Maranya
Catheter-associated urinary tract infection (CAUTI) is one of the most common healthcare associated infections, posing a significant challenge in clinical settings. This study develops a mathematical model that incorporates bacterial contamination to investigate the transmission dynamics of CAUTI. We derive the disease-free and endemic equilibria and compute the basic reproduction number, 0, using the next generation matrix method. The model’s well-posedness is examined through the existence and uniqueness of solutions, and the long-term behavior is analyzed to determine the stability of the equilibria. To assess the relative importance of the model parameters, we conduct a global sensitivity analysis using the extended Fourier Amplitude Sensitivity Test (eFAST) method. The results identify the catheterization rate (𝜔), catheter removal rate (𝛿), and transmission coefficients (𝜙, 𝛽1, 𝛽2) as the most influential parameters affecting infection dynamics. These findings highlight key intervention targets for controlling CAUTI. The model also serves as a foundation for future extensions, including the incorporation of asymptomatic carriers and environmental sanitation interventions.
Farm-level yield prediction for maize, rice, and beans in Tanzania using machine learning and multi-source agricultural data
(Elsevier B.V., 2026-02-19) Omary, Bally; Dida, Mussa; Nyambo, Devotha
Accurate predictions of crop yields at the farm level are essential for improving agricultural productivity, enhancing food security, and supporting informed decision-making among smallholder farmers. However, conventional field assessments and simple statistical models are often time-consuming, limited in scope, and unable to capture complex interactions among climatic and soil factors. To address these challenges, this paper proposes a machine learning-based model for predicting the productivity of multiple crops, including maize, rice, and beans, using multi-source farm-level data from Tanzania. The dataset integrates climate variables such as temperature and rainfall, soil type, farm size, and crop type. Four ensemble learning models, namely Random Forest, Gradient Boosting, Extreme Gradient Boosting, and Extra Trees, were evaluated using an 80/20 train–test split on 9,897 farm-level records acquired from the Mbeya, Ruvuma, and Songwe regions between 2022 and 2024. Hyperparameter tuning with a fivefold cross-validation was applied to improve model generalization and reduce overfitting. Among the evaluated models, the Extra Trees ensemble achieved the highest performance, with a pooled multi-crop R² of 95%, while crop-specific R² values ranged from 79% to 81% for maize, rice, and beans. These findings demonstrate the potential of the proposed approach to support farm-level cultivation planning and climate adaptation decisions for smallholder farmers.
Evaluating the Impact of Fog on Free Space Optical Communication Links in Mbeya and Morogoro, Tanzania
(MDPI, 2026-01-25) Tarimo, Catherine; Rashidi, Florence; Kaijage, Shubi
Free-space optical (FSO) communication is a promising alternative to radio-frequency (RF) and optical fiber systems due to its high data rates and large bandwidth. How ever, its performance is highly susceptible to atmospheric conditions such as fog, rain, snow, and haze. This paper analyzes fog-induced signal attenuation in the Morogoro and Mbeya regions of Tanzania using the Kim and Kruse attenuation models. To improve link performance, a quadrature amplitude modulation (QAM) multiple-input multiple output (MIMO) FSO link was designed and analyzed using OptiSystem 22.0. In Mbeya, light fog conditions with 0.5 km visibility resulted in an attenuation of 32 dB/km, a bit error rate (BER) of 4.5 × 10−23, and a quality factor of 9.79 over a 2.62 km link. In Mo rogoro, dense fog with 0.05 km visibility led to an attenuation of 339 dB/km, a BER of 1.12 × 10−15, andamaximumlinkrangeof0.305km. Experimentalmeasurementswerefur ther conducted under clear, moderate, and dense fog conditions to systematically evaluate the FSO link performance. The results demonstrated that MIMO techniques significantly enhanced link performance by mitigating fog effects. Moreover, a dedicated application was developed to analyze transmission errors and evaluate system performance metrics. Additionally, a mathematical model of the FSO link was developed to describe and forecast the performance of the MIMO FSO system in atmospheric conditions impacted by fog.
Control strategies for the dynamics of catheter-associated urinary tract infection
(Elsevier, 2026-02-21) John, Innocent; Stephano, Mussa; Mayengo, Maranya
Catheter-associated urinary tract infections (CAUTI) remain a major challenge in healthcare, particularly among hospitalized and long term catheterized patients. This study develops a deterministic compartmental model integrated with optimal control theory to evaluate the effects of three time dependent interventions: public health education, alternative catheteriza tion methods, and environmental hygiene control. Unlike existing CAUTI models, the proposed framework explicitly incorporates both host to host transmission and environmental contami nation, and quantifies intervention effectiveness using Pontryagin’s Maximum Principle and the forward backward sweep algorithm. Simulation results show that the combined application of all three controls yields the highest reduction in disease burden, decreasing infection preva lence by approximately 82%, catheterized individuals by 75%, and environmental bacterial concentration by 85% within 60 days compared to the uncontrolled scenario. Among dual interventions, education with environmental hygiene achieves a 68% reduction in infections, followed by catheterization reduction with hygiene at 63%. Education with catheterization reduction produces a smaller decline of 49%. For individual interventions, environmental hygiene is the most effective, achieving a 58% reduction, followed by education 46% and catheterization minimization 32%. Closed-form threshold conditions derived from the effective reproduction number (𝑒) provide practical bounds for control intensities needed to ensure 𝑒 < 1, particularly highlighting minimum hygiene requirements. Optimal-control profiles indicate high initial intervention intensity that declines as infections decrease. Overall, the findings demonstrate that integrated control especially when environmental hygiene is included offers the most impactful strategy for r
Agent-based simulation of seasonal malaria chemoprevention strategy in Southern Tanzania: comparing dihydroartemisinin-piperaquine with or without primaquine
(Springer Nature, 0026-02-13) Mfala, Celina; Nyambo, Devotha; Mwaiswelo, Richard; Tchuenche, Jean; Clemen, Thomas
Background The effect of seasonal malaria chemoprevention (SMC) strategy on malaria transmission using a single low dose of primaquine (SLDPQ) added to artemisinin-based combination therapy has not been established in Africa. An agent-based model and simulation (ABMS) was used to assess SMC effectiveness using dihydroartemisinin-piperaquine (DP) with and without SLDPQ in Masasi and Nanyumbu Districts, Tanzania. Methods ABMS was developed in AnyLogic platform using secondary data from a cluster-randomized DP-based SMC study conducted in the districts, to assess the effectiveness of DP with and without SLDPQ for control of malaria in under-five children. The model incorporated human, mosquito, transmission, intervention, and environment sub-models, and simulated three monthly rounds of SMC over a 180-day period. Environment temperature, an important factor in mosquito breeding was simulated in three scenarios, first using average field temperature, and then when it was increased or decreased by 10C from the average. Model outputs were compared with field results to evaluate external validity. Results Overall, 2275 participants, 1135 in the intervention and 1140 in the control arm were involved in the model. At baseline, malaria prevalence was 11.5% (130/1135) and 16.3% (186/1140) in the intervention and control arm, respectively. At the end of 125-day simulation period malaria prevalence declined to 4.1% (47/1135), and it rebounded to 7.1% (80/1135) at the end of 180-day simulation period after three rounds of DP alone administration. Addition of SLDPQ to DP led to a further declined of the prevalence to 1.4% (16/1135) and 3.9% (44/1135) at the end of 125-day and 180-day, respectively. In the DP alone, the increase in average temperature by 1˚C further decreased malaria prevalence to 2.6% (30/1135) and 5.0% (57/1135) at the end of 125-day and 180-day, respectively, whereas the decrease of temperature by 1 ˚C decreased the malaria prevalence to 3.2% (36/1135) and 4.2% (48/1135) at the end of 125-day and 180-day, respectively. Conclusions The ABMS has demonstrated that addition of SLDPQ to DP reduced malaria transmission significantly regardless of the increase or decrease of the average temperature by 1 ˚C. SLDPQ can be added to DP-SMC and scaled-out for the control of malaria in Tanzania.
A stacking model integrating GARCH and LSTM with feature interactions for time series volatility prediction
(Elsevier Inc., 2026-02-04) Peter, Michael; Mirau, Silas; Sinkwembe, Emmanuel; Kasumo, Christian; Guambe, Calisto
Volatility forecasting remains a cornerstone of quantitative finance, underpinning risk management, portfolio optimization, and regulatory oversight. This study introduces a novel stacking model that integrates the generalized autoregressive conditional heteroskedasticity (GARCH) framework with long short-term memory (LSTM) networks to capture both econometric structure and nonlinear temporal dependencies in finan cial time series. Unlike conventional hybrid approaches that sequentially cascade outputs, the proposed framework employs GARCH and LSTM as parallel base learners, with their predictions intelligently fused through a meta-learner that exploits feature interactions and cross-model synergies. The empirical evaluation benchmarks the stacking ensemble against state-of-the-art alternatives, including DLINEAR, CKAN, N-BEATS, and individual GARCH and LSTM specifications across multiple performance metrics. Results demonstrate consistent superiority across RMSE, MAE, accuracy, RAMP, geometric mean, Hausdorff distance, and AUC metrics, validating the synergistic benefits of integrating econometric and machine learning paradigms within a theoretically grounded architecture. The model’s superior performance stems from leveraging GARCH’s parametric efficiency in modeling volatility clustering while harnessing LSTM’s capacity to capture complex nonlinear temporal patterns. Beyond methodological contributions, the framework offers practical value for enhancing systemic risk monitoring, improving stress testing frameworks, and optimizing investment strategies across diverse market conditions. The demonstrated robustness across different market regimes underscores its potential for adoption in both routine operations and crisis contexts. This research establishes stacking-based ensemble modeling as a powerful paradigm for advancing volatility prediction and provides a foundation for next-generation financial forecasting systems.

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