The proper application of retrosynthesis to identify possible transformations for a given target compound requires a lot of chemistry knowledge and experience. However, because the complexity of this technique scales together with the complexity of the target, efficient application on compounds with intricate molecular structures becomes almost impossible for human chemists. The idea of using computers in such situations has existed for a long time, but the accuracy was not sufficient for practical applications. Nevertheless, with the steady improvement of machine learning and artificial intelligence in recent years, computer-assisted retrosynthesis has been gaining research attention again. Because of the overall lack of chemical reaction data, the main challenge for the recent retrosynthesis methods is low exploration ability during the analysis of target and intermediate compounds. The main goal of this research is to develop a novel, template-free approach to address this issue. Only individual molecular substructures of the target are used to determine potential disconnection sites, without relying on additional information such as chemical reaction class. The model for the identification of potential disconnection sites is trained on novel molecular substructure fingerprint representations. For each of the disconnections suggested using the model, a simple structural similarity-based reactant retrieval and scoring method is applied, and the suggestions are completed. This method achieves 47.2% top-1 accuracy for the single-step retrosynthesis task on the processed United States Patent Office dataset. Furthermore, if the predicted reaction class is used to narrow down the reactant candidate search space, the performance is improved to 61.4% top-1 accuracy.

In all information systems it is very important to operate with correct information. Incorrect information can lead to many problems that can cause direct financial and reputation loss of the company. Data used by the system can be gathered by sensors, scripts or by hand. In all those cases, mistakes are possible. It is important to detect mistakes on time and stop them from propagating further into the system. In this paper, a novel multi-step anomaly detection algorithm based on the greatest common divisor and median value is described. The algorithm for anomaly detection in historical sales data is used as a part of the smart warehouse management system which is implemented in some of the largest distribution companies in Bosnia and Herzegovina. The algorithm showed significant results in anomaly detection on company orders and improved a number of processes in the operation of the smart warehouse management system. The algorithm described can also be used in other areas where the transaction data is collected, such as sales and banking,

One of the frequently occurring tasks during the development of a warehouse management system is the implementation of a routing algorithm of some kind. Whether it is for guiding workers during order picking, routing delivery vehicles or for routing company representatives, this task has proven to be challenging in the technical as well as the social sense. In other words, the task is heavily dependent on various company-specific constraints and it directly dictates the way employees should do their job. This paper describes a strategic approach to the development and gradual integration process of such algorithms which makes sure that all constraints are satisfied and, more importantly, ensures that route suggestions are viewed by the employees as a helpful tool rather than a threat to their job. Described through a real-world case study in a medium-to-large warehouse, the routing efficiency is almost doubled in comparison to the previous approach and critical factors are analysed and discussed throughout different stages of the process.

One of the frequently occurring tasks during the development of warehouse management systems is the implementation of routing algorithms of some kind. Whether it is for routing workers during order picking, delivery vehicles or company representatives, this task has proven to be challenging in the technical as well as the social sense. In other words, the task is heavily dependent on various general and company-specific constraints and it directly dictates the way employees should do their job. This paper describes a strategic approach to the development and gradual integration of such algorithms which makes sure that all constraints are satisfied and, more importantly, ensures that route suggestions are viewed by the employees as a helpful tool rather than a threat to their job. In the first part of this paper, the approach is described and evaluated on a warehouse representative routing problem through a real-world case study in a medium-to-large warehouse. In the second part, the same approach is adapted to a delivery vehicle routing problem for a smaller retailer company. In both cases, routing efficiency almost doubled in comparison to previous approaches used by the companies. The most important factors of the implementation and integration stages as well as the impact of the changes on employee satisfaction are aggregated, analysed in detail, and discussed throughout different stages of development.

In modern market it is very important to deliver products to customers fast. That delivery can be on site or to customer's homes. In order to achieve that it is important to have enough goods stored in warehouses and prepared for delivery. It is not a good decision to clutter up warehouses with the goods because space is limited and expensive and it makes it more complicated to collect orders. Those are the reasons why it is important that number of stored goods converge to the exact number of product units that will be ordered in the future. Demand forecasting tries to solve that problem. In this work demand forecasting algorithm based on Long Short-Term Memory recurrent neural network is described and compared with demand forecasting algorithms developed by authors before.

Building a successful warehouse management system encompasses solving many problems of different nature to reshape the general workflow and ensure improvements in terms of resource management. In order for such a system to be accepted and used by a logistics company, those solutions need to be presented through a simple, adaptable and most importantly, a feasible software solution. One of the aspects that needs to be covered while building a warehouse management system is the optimal product placement in the warehouse. If the products are strategically placed, all the other improvement strategies like stock to picking zone item transfer and item picking order become more efficient and easier to implement. In this paper, tactics and issues regarding the optimal product placement in a warehouse are analyzed in detail through a real-world case study. Solutions of this problem largely differ for new, empty warehouses and for already operative warehouses not able to carry out a stocktaking process. Approaches for both possible situations are proposed and tested out on two different warehouses in a medium-to-large logistics company.

The warehouse layout directly affects the process of receiving and storing of goods, as well as the order picking process. A standard layout has its pallet places arranged in parallel shelves with aligned cross-aisles. However, a big number of warehouses use a shelf layout in which the complete warehouse cannot be represented as a sequence of parallel shelves. Effective calculation of distances between positions in the warehouse presents a significant step towards the effective order collection and distribution of goods. This paper describes a generic approach for calculating distances within a warehouse which does not necessarily have standard layout. It describes the application of the algorithm on different warehouse layouts in which the shelves can be split into smaller units so that inside of each unit the shelves are distributed in a standard way. Dynamic programming was used for the calculation of distances inside of those units. An analysis and testing of the algorithm were performed on two middle-sized warehouses with non-standard shelf layout. The algorithm was tested in the process of collecting orders, as well as the process of moving goods from the stock to the pick zone.

Efficient detection of distresses on asphalt pavements has a great impact on safe driving, thus it has been very active research subject in recent years. High severity level distresses, such as potholes, are the most severe threat to safe driving, hence timely detecting and repairing potholes is crucial in ensuring safety and quality of driving. Existing methods often require sophisticated equipment and algorithms with high-computational pre-processing steps for analysis of substantial amount of existing data (images or videos). In this paper, a new unsupervised method for detection of high severity distresses on asphalt pavements was proposed. The method was tested on highly unstructured image data set captured from different cameras and angles, with different irregular shapes and number of potholes to demonstrate its capability. Results indicated that the method can be used for rough detection and estimation of damaged pavements.

Warehouse Management Systems (WMS) play a major role in optimizing warehouse logistic processes, archiving merchant trends of supply and demand, and also easen treating of goods which are close to expiring deadline, out of stock, broken or deposited by customers. Since warehouse management systems may be integrated with business intelligence, they may comprise algorithms for optimization of warehouse order picking and storing of goods, sales prediction etc. In this article case study of warehouse management system is presented, and afterwards problem of optimal warehouse order picking is exhibited. This problem is transformed into Travelling Salesman Problem (TSP), and solved using heuristic optimization methods.

Implementing a successful warehouse management system from scratch contains many challenges of different nature. The most important aspect is the financial status of the company that is implementing the system. How many resources is the company willing to spend is always a difficult dilemma. The second important issue is the actual usefulness of the developed solution. If the system cannot be implemented in the real world, as advanced as it may be in theory, it has very little or no use at all. In this paper, the current state of the picking zone of a medium-to-large logistics company warehouse is analyzed through a real-world case study. On the top of the collected results, a method for the optimal strategic and quantitative item placement in the picking zone is constructed through the usage of fitting algorithms. One month after the method was released to production, a quality check of the system by supervising the warehouse activities of the picking zone is conducted and the results are analyzed.

The prediction of protein secondary structure is the method of finding the way in which an amino acid sequence causes the protein structure to fold and bend into alpha helices, beta strands and other shapes. Until today, the problem of finding protein secondary structure is not fully resolved. Classification or clusterization based methods have an accuracy rate of circa 80 percent and they mainly work on a reduced set of shapes and folds. It is very difficult to predict how a local sequence of amino acids is going to behave and in which way it is going to affect the future of protein structure. Based upon the predicted secondary structure of the protein, the tertiary and quaternary predictions show the real nature and function of the protein as a whole. In this paper, we address the problem of the secondary structure prediction of protein and propose a new hybrid method based on the usage of multiple neural networks with the use of a consensus function and compare our approach with other efficient methods.

Autorska djela „sirocadi“ su takve intelektualne tvorevine za koje je izvjesno da su kvalifikovane za autorskopravnu zastitu po pozitivnim normama autorskog i srodnih prava, povodom kojih je, dalje, izvjesno da nije istekao vremenski period zastite, ali, povodom kojih autor ili drugi nosilac autorskih imovinskih ili drugih prava nije poznat ili ne može da bude utvrđen, odnosno jeste poznat ili može biti utvrđen, ali gdje nakon onoga sto clan 2 st. 1 Direktive 2012/28/EU Evropskog parlamenta i Savjeta donesene 25. oktobra 2012. godine o izvjesnim dozvoljenim upotrebama autorskih djela „sirocadi“ zove „diligentnom“ potragom autor ili nosilac prava ne može da se pronađe radi davanja odobrenja za zasticenu upotrebu tog autorskog djela. Britanska Biblioteka procjenjuje da 40% njene kolekcije predstavljaju autorska djela „sirocadi“. Za istraživace, arhiviste kao i bilo koja druga lica koja žele da koriste autorska djela „sirocadi“ ali koja to ne mogu buduci da ne mogu da ne mogu da nađu ovlasteno lice za davanje dozvole postojanje ovih vrsta autorskih djela ali nepostojanje mehanizma za njehovu legalnu upotrebu predstavlja neprihvatljivo ugrožavanje pravne sigurnosti u sitemu zastite autorskog i srodnih prava, te neopravdano izuzimanje velikog postotka i broja intelektualnih tvorevina iz kulturoloskog, naucnog i stvaralackog diskursa drustva. Rad pocinje sa pojmovnim određenjem autorskog djela „sirocadi“ te uticajem postojanja ovih vrsta autorskih djela na stvaralacki potencijal i opcenito na drustvo. Analiziraju se pozitivnopravna pravila u Zakonu o autorskom i srodnim pravima (Sl. glasnik BiH 63/10) koja se mogu primjeniti na iteracije autorskih djela ove vrste. Izvrsava se analiza direktive 2012/28/EU, te se na kraju donosi zakljucak o tome kako i u kom obimu Bosna i Hercegovina bi trebala da transponira ovu direktivu u svoj pozitivnopravni sistem zastite autorskog i srodnih prava, a sa ciljem uklanjanja pravno neželjenih dejstava koje njihovo postojanje uzrokuje. Kljucne rijeci: autorsko djelo „siroce“, Direktiva 2012/28/EU, diligentna potraga, nepoznat autor, Bosna i Hercegovina