Defence Terrain Research Laboratory

Introduction : La vaccination permet d’immuniser les enfants contre les maladies meurtrières et invalidantes. Selon l’organisation mondiale de la santé, la plupart des enfants incomplètement vaccinés se trouvent en Afrique. Au Mali, 55% des enfants de 12-23 mois étaient complètement vaccinés et 14% n’avaient reçu aucun vaccin en 2018. Notre étude avait pour objectif de déterminer les facteurs associés à la vaccination incomplète des enfants de 15-23 mois de Tominian dans un contexte d’insécurité en 2020. Méthodes : Nous avons mené une étude transversale analytique sur 508 mères d’enfants âgés de 15-23 mois. Un échantillonnage stratifié à trois degrés a permis d’identifier les aires de santé, villages et mères d’enfants. Nous avons testé l’association entre la vaccination et ses déterminants probables en utilisant une régression logistique multiple à un niveau de signification de 0,05 et un intervalle de confiance à 95% (IC95%) à l’aide d’Epi-info 7.2.2 et de Medcalc. Le test de Hosmer et Lemeshow a été utilisé pour juger l’adéquation du modèle de régression multiple obtenu après l’analyse multivariée. Résultats : L’âge moyen des mères et des enfants était respectivement de 27±6 ans et de 19±3 mois. La couverture vaccinale de la 2ème dose de vaccin antirougeoleux était de 34,8%. La méconnaissance de l’importance de la vaccination (ORa[IC95%]=9,3[2,2-40,6], p=0,003), la connaissance de moins cinq maladies cibles de la vaccination (ORa[IC95%]=3,8[1,4-10,2 ], p˂0,008), l’absence de revenue (ORa=[IC95%]=2,9[1,7-5,2], p˂0,001), le temps d’attente long (ORa=[IC95%]=2,4[1,5-3,7], p˂0,001), l’accouchement à domicile (ORa[IC95%]=2,3[1,5-3,5], p˂0,001) et la non tenue de causeries éducatives par les vaccinateurs (ORa=[IC95%]=1,8[1,2-2,8], p=0,009) étaient statistiquement associés à la vaccination incomplète des enfants. Conclusion : La couverture vaccinale n’était pas associée à l’insécurité. Nous recommandons la mise en œuvre des activités de rattrapage vaccinale, de sensibiliser et de soutenir la création d’activités génératrices de revenue pour les mères afin de contribuer à l’amélioration du statut vaccinal des enfants.

Post-traumatic stress disorder (PTSD) and Stockholm syndrome are two distinct psychological responses that could occur following intense trauma. While PTSD is characterized by intrusive memories, hypervigilance, and avoidance behaviors, Stockholm syndrome involves the paradoxical development of emotional attachment to the abuser who inflicted the trauma. This case report presents a 39-year-old female patient who survived a life-threatening neck stabbing incident by her abusive ex-husband and developed both PTSD and Stockholm syndrome. Initially presenting with classic PTSD symptoms such as hyper-arousal, intrusive memories, and anxiety, the patient's psychological recovery was complicated by a growing emotional attachment to her abuser. Over the course of treatment, her PTSD symptoms improved, but she expressed empathy for her attacker, maintained contact with him while he was incarcerated, and even sought to drop charges against him. This case highlights the complex interplay between PTSD and Stockholm syndrome, suggesting that PTSD symptom resolution may contribute to or even coincide with the development of Stockholm syndrome, particularly in cases of intimate partner violence (IPV). This report also recognizes the need for clinicians to recognize the potential emergence of Stockholm syndrome during PTSD recovery and to consider its implications for treatment and recovery in IPV cases.

Toxic epidermal necrolysis (TEN) is a severe, often drug-induced mucocutaneous reaction characterized by widespread epidermal necrosis and detachment. Ocular involvement is common and can lead to significant morbidity if not addressed promptly. This case report presents a 10-year-old female with fever, rash, and severe ocular symptoms. Early use of Prokera, an amniotic membrane device, was instrumental in managing her ocular condition. After discharge and follow-up care, the patient showed marked improvement. This case highlights the importance of early recognition and intervention in ocular manifestations of TEN to prevent long-term complications.

Background The anterior cruciate ligament (ACL) is a crucial connective tissue that links the femur to the tibia, playing a vital role in stabilizing the knee by resisting forward and rotational movements. ACL tears can occur due to both contact and non-contact sports injuries. Diagnosis and assessment typically involve the Lachman test and magnetic resonance imaging. Initial treatment focuses on reducing swelling, followed by physical therapy or surgery to restore long-term knee functionality. Objective This study aimed to assess the injury prevalence and awareness of ACL and the engagement of preventing training programs among Bahraini athletes across diverse sports. Methods A cross-sectional survey was utilized to evaluate injury prevalence and awareness of ACL and engagement in preventing training among 161 Bahraini athletes from different sports. Data were collected through a detailed questionnaire addressing demographics, sports involvement, ACL injury history, and preventive training. The analysis involved descriptive statistics, one-way ANOVA, and independent t-tests to compare knowledge across sports types. A chi-square test was performed to examine correlations between injury history and training. Results The results showed that athletes in full-contact sports exhibited significantly higher ACL injury awareness compared to those in semi-contact and non-contact sports. Furthermore, a higher percentage of athletes in semi-contact and non-contact sports reported a history of ACL injuries when compared to those in full-contact sports. However, there was no significant difference in the engagement of preventive exercise training across different sports categories. Conclusion A notable gap in ACL injury awareness exists among Bahraini athletes, varying significantly across sport types. The data highlight the need for customized educational programs catering to different sports.

Landslide incidence is common in hilly areas. In particular, Tangni in Uttrakhand state between Pipalkoti and Joshimath has experienced a number of landslide incidents in the recent past. Thus, it is important to forecast slope-movements and associated landslide events in advance of their occurrence to avoid the associated risk. A recent approach to predicting slope-movements is by using machine-learning techniques. In machine-learning literature, moving-average methods (Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Autoregressive (AR) model), Lazy methods (Instance-based-k (IBk) and Locally Weighted Learning (LWL)) and information-gain methods (REPTree and M5P) have been proposed. However, a comparison of these methods for real-world slope-movements has not been explored. The primary objective of this paper is to compare SARIMA, AR, LWL, IBk, REPTree and M5P methods in their ability to predict soil-movements recorded at the Tangni landslide in Chamoli, India. Time-series data about slope-movements from five-sensors placed on the Tangni landslide hill were collected daily over a 78-week period from July 2012 to July 2014. Different model parameters were calibrated to the training data (first 62-weeks) and then made to forecast the test data (the last 16-weeks). Results revealed that the moving-average models (SARIMA and AR) performed better compared to the lazy and information-gain methods during both training and test. Specifically, the SARIMA model possessed the smallest error compared to other models in test data. We discuss the implications of using moving-average methods in predicting slope-movements at real-world landslide locations.KeywordsSARIMAInstance-basedLocally weighted learningM5PREPTreeTangni landslide

Abstract In the current uncertainty, people are losing trust in dialogue, and increasingly turning to hard bargaining and protests. The increasing use of drone technology can contribute to this negative shift in attitudes around oil and gas installations. At the same time the technology offers a unique opportunity for engagement. The Social Impact Asset Management (SIAM) project is a concession area drone and human data collection platform. It is a way to present easily understood evidence for joint decision making. By also facilitating the management of regulatory requirements and planning, it creates much needed scalability in drone use. The angle taken in this presentation taps into work done for safety and rescue services in the UK, and impact assessment for the United Nations. It revolves around how to deal with the two challenges to the use of drones in sustainability: scaling up, and social acceptance. It maps the way in which stakeholder engagement will benefit, and how drones can be unleashed for the benefit of local populations and industry. It confirms that today’s digital revolution has led to a decisive moment. Innovative solutions can make this a positive one. SIAM is the result of a strategic alliance between Social Terrain Ventures and the Thales Soarizon project, a civilian application of advanced aerospace technology. It has not yet been applied in oil and gas, but the case for the application is clear.

Abstract Cross-border infiltration presents a national concern. Defense and monitoring of border areas require the inclusion of a wide range of tactics to interdict infiltrators. In an infiltration situation, human habitats along the infiltration route present numerous advantages to an infiltrator. The security personnel can adopt various strategies with a habitat to ambush the infiltrator since it is highly likely for the infiltrator to visit a habitat. The habitat acts as a proactive ambush site for the defender and therefore presents him with a high probability of deceiving the infiltrating agent. In this paper, a two-player zero-sum game is proposed between a defender and an infiltrator for habitat selection under the threat of ambush with and without deception. Both players apply various strategies to maximize their benefit out of the options available to them and adjust to each other’s options over the play only to eventually settle on an optimum strategy. The two players determine the tactical options available to them. The ambusher assigns a numerical value (Measure of Effectiveness, MOE) to each possible outcome by judging the potential gain or loss from an encounter. With the proposed game models the players can check to see if the outcome of the encounter in either deceptive or non-deceptive scenarios favors him or the opponent and what optimum strategy is possible with a desirable outcome. To the best of our knowledge, the present paper is the first application of game theory to human–human interaction similar to a predator–prey scenario for habitat selection.KeywordsHabitat selectionAmbushDeceptionGame theory

Landslides plague the Himalayan region, and landslide occurrence is widespread in hilly areas. Thus, it is important to predict soil movements and associated landslide events in advance of their occurrence. A recent approach to predicting soil movements is to use machine-learning techniques. In machine-learning literature, both moving-average-based methods (Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Autoregressive (AR) model) and support-vector-based methods (Sequential Minimal Optimization; SMO) have been proposed. However, an evaluation of these methods on real-world landslide prediction has been little explored. The primary objective of this paper is to compare SARIMA, AR, and SMO methods in their ability to predict soil movements recorded at a real-world landslide site. A SARIMA model, an AR model, and a SMO model were compared in their ability to predict soil movements (in degrees) at the Tangni landslide in Chamoli, India. Time-series data about soil movements from five-sensors placed on the Tangni landslide hill were collected daily over a 78-week period from July 2012 to July 2014. Different model parameters were calibrated to the training data (first 62-weeks) and then made to predict the test data (the last 16-weeks). Results revealed that the moving-average models (SARIMA and AR) performed better compared to the support-vector models (SMO) during both training and test. Specifically, the SARIMA model possessed the smallest error compared to the AR and SMO models during test. We discuss the implications of using moving-average methods in predicting soil movements and associated landslides at real-world landslide locations.

Video summarization is a process of creating and presenting a meaningful abstract view of entire video within a reduced period of time. Video summarization facilitates quick browsing of activities present in longer video. Here we are presenting an automatic video summarization method to create a summarized video of variable length from original video. Summarized video is a highly temporally condensed representation of video to give an overview of different activities present in the video so that we are extracting multiple events from original video to show multiple activities in original form. Here we are using motion of object and object tracking using blob matching over a video sequence. Summarized video also preserves chronological order i.e. relative timing between activities.