2型糖尿病（T2DM）中持续的高水平血糖可能会带来灾难性的长期健康后果。 T2DM临床干预措施的重要组成部分是监测饮食摄入，以使血浆葡萄糖水平保持在可接受的范围内。然而，当前监测食物摄入的技术是时间密集的，容易出错。为了解决这个问题，我们正在开发使用连续葡萄糖监测器（CGM）自动监测食物摄入量和这些食物组成的技术。本文介绍了一项临床研究的结果，其中参与者佩戴CGM时，参与者消耗了9份标准营养素的标准餐（碳水化合物，蛋白质和脂肪）。我们构建了一个多任务神经网络，以估计CGM信号的大量营养素组成，并将其与基线线性回归进行了比较。最好的预测结果来自我们提出的神经网络，该神经网络受试者依赖性数据训练，如均方根相对误差和相关系数所衡量。这些发现表明，可以从CGM信号中估算大量营养素组成，从而开发了开发自动技术以跟踪食物摄入量的可能性。

The problem of approximating the Pareto front of a multiobjective optimization problem can be reformulated as the problem of finding a set that maximizes the hypervolume indicator. This paper establishes the analytical expression of the Hessian matrix of the mapping from a (fixed size) collection of $n$ points in the $d$-dimensional decision space (or $m$ dimensional objective space) to the scalar hypervolume indicator value. To define the Hessian matrix, the input set is vectorized, and the matrix is derived by analytical differentiation of the mapping from a vectorized set to the hypervolume indicator. The Hessian matrix plays a crucial role in second-order methods, such as the Newton-Raphson optimization method, and it can be used for the verification of local optimal sets. So far, the full analytical expression was only established and analyzed for the relatively simple bi-objective case. This paper will derive the full expression for arbitrary dimensions ($m\geq2$ objective functions). For the practically important three-dimensional case, we also provide an asymptotically efficient algorithm with time complexity in $O(n\log n)$ for the exact computation of the Hessian Matrix' non-zero entries. We establish a sharp bound of $12m-6$ for the number of non-zero entries. Also, for the general $m$-dimensional case, a compact recursive analytical expression is established, and its algorithmic implementation is discussed. Also, for the general case, some sparsity results can be established; these results are implied by the recursive expression. To validate and illustrate the analytically derived algorithms and results, we provide a few numerical examples using Python and Mathematica implementations. Open-source implementations of the algorithms and testing data are made available as a supplement to this paper.